Long-read RNA sequencing identifies non-coding isoform switching as a regulator of cell fate
Bibliographic record
Abstract
Abstract Development of long-read RNA sequencing technologies has paved the way to the exploration of RNA isoform diversity and its relevance in regulating cell fate. However, identifying new functional isoforms is still very difficult. Here, we leverage long-read RNA sequencing to study changes in isoforms during somatic cell reprogramming and identify novel isoforms occurring throughout cell state transitions. We demonstrate tight regulation of non-coding isoforms and show that isoform switching plays previously overlooked functional roles and outcomes in gene regulation and cell fate changes. We uncover a novel long non-coding RNA, Snhg26 , that undergoes isoform switching during reprogramming to enhance the conversion of differentiated cells towards the pluripotent state. Knock-down of Snhg26 in mouse and human pluripotency models reveals that it is important for pluripotency acquisition. Together, our study provides a resource to study full-length isoform usage during cell fate change. It also demonstrates the power of long-read sequencing to identify functionally relevant gene isoforms in the context of cell plasticity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".